{
 "cells": [
  {
   "attachments": {
    "49860e47-cfb0-4ad8-8edd-9bbc7e6d1b0c.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "id": "96e76ef2",
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "papermill": {
     "duration": 0.028759,
     "end_time": "2021-08-16T21:43:44.071108",
     "exception": false,
     "start_time": "2021-08-16T21:43:44.042349",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Kaggle’s 30 days of machine learning:\n",
    "## Scikit-optimize for LightGBM (regression tutorial)\n",
    "\n",
    "![image.png](attachment:49860e47-cfb0-4ad8-8edd-9bbc7e6d1b0c.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb7f782e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-16T21:01:09.763519Z",
     "iopub.status.busy": "2021-08-16T21:01:09.762679Z",
     "iopub.status.idle": "2021-08-16T21:01:09.773168Z",
     "shell.execute_reply": "2021-08-16T21:01:09.771627Z",
     "shell.execute_reply.started": "2021-08-16T21:01:09.763465Z"
    },
    "papermill": {
     "duration": 0.028256,
     "end_time": "2021-08-16T21:43:44.128102",
     "exception": false,
     "start_time": "2021-08-16T21:43:44.099846",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Machine learning algorithms have lots of knobs, and success often comes from twiddling them a lot.**\n",
    "\n",
    "*(DOMINGOS, Pedro. A few useful things to know about machine learning. Communications of the ACM, 2012, 55.10: 78-87.)*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d2e798b",
   "metadata": {
    "papermill": {
     "duration": 0.026344,
     "end_time": "2021-08-16T21:43:44.180442",
     "exception": false,
     "start_time": "2021-08-16T21:43:44.154098",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "In optimizing your model for this competition, you may wonder if there is a way to:\n",
    "\n",
    " * Leverage the information that you get as you explore the hyper-parameter space\n",
    "\n",
    " * Not necessarily become be an expert of a specific ML algorithm\n",
    "\n",
    " * Quickly find an optimization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e14a1b5",
   "metadata": {
    "papermill": {
     "duration": 0.025573,
     "end_time": "2021-08-16T21:43:44.231708",
     "exception": false,
     "start_time": "2021-08-16T21:43:44.206135",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The answer is:\n",
    "\n",
    "**Bayesian Optimization** (*SNOEK, Jasper; LAROCHELLE, Hugo; ADAMS, Ryan P. Practical bayesian optimization of machine learning algorithms. In: Advances in neural information processing systems. 2012. p. 2951-2959*)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "711a05d0",
   "metadata": {
    "papermill": {
     "duration": 0.029446,
     "end_time": "2021-08-16T21:43:44.292517",
     "exception": false,
     "start_time": "2021-08-16T21:43:44.263071",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The key idea behind Bayesian optimization is that we optimize a proxy function (the surrogate function) instead than the true objective function (what actually grid search and random search both do). This holds if testing the true objective function is costly (if it is not, then we simply go for random search.\n",
    "\n",
    "Bayesian search balances exploration against exploitation. At start it randomly explores, doing so it builds up a surrogate function of the objective. Based on that surrogate function it exploits an initial approximate knowledge of how the predictor works in order to sample more useful examples and minimize the cost function at a global level, not a local one.\n",
    "\n",
    "Bayesian Optimization uses an acquisition function to tell us how promising an observation will be. In fact, to rule the tradeoff between exploration and exploitation, the algorithm defines an acquisition function that provides a single measure of how useful it would be to try any given point."
   ]
  },
  {
   "attachments": {
    "f8d4cbb2-09ef-4c55-a3f9-a93e6ddd8033.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "id": "636bb306",
   "metadata": {
    "papermill": {
     "duration": 0.027913,
     "end_time": "2021-08-16T21:43:44.347864",
     "exception": false,
     "start_time": "2021-08-16T21:43:44.319951",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "![image.png](attachment:f8d4cbb2-09ef-4c55-a3f9-a93e6ddd8033.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6707d157",
   "metadata": {
    "papermill": {
     "duration": 0.025691,
     "end_time": "2021-08-16T21:43:44.400428",
     "exception": false,
     "start_time": "2021-08-16T21:43:44.374737",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "In this step by ste tutorial, you will deal Bayesian optimization using LightGBM in few clear steps:\n",
    "\n",
    "1. Prepare your data, especially your categorical\n",
    "2. Define your cross-validation strategy\n",
    "3. Define your evaluation metric\n",
    "4. Define your base model\n",
    "5. Define your hyper-parameter search space\n",
    "6. Run optimization for a while"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7173394",
   "metadata": {
    "papermill": {
     "duration": 0.025407,
     "end_time": "2021-08-16T21:43:44.451635",
     "exception": false,
     "start_time": "2021-08-16T21:43:44.426228",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 1. Data preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fad8bcb1",
   "metadata": {
    "papermill": {
     "duration": 0.025665,
     "end_time": "2021-08-16T21:43:44.503280",
     "exception": false,
     "start_time": "2021-08-16T21:43:44.477615",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "As first steps:\n",
    "\n",
    "we load the train and test data \n",
    "we separate the target from the training data\n",
    "we separate the ids from the data\n",
    "we convert integer variables to categories (thus our machine learning algorithm can pick them as categorical variables and not standard numeric one)\n",
    "\n",
    "You can add further processing, for instance by feature engineering, in order to succeed in this competition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ac249046",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-16T21:43:44.559102Z",
     "iopub.status.busy": "2021-08-16T21:43:44.557991Z",
     "iopub.status.idle": "2021-08-16T21:43:46.816429Z",
     "shell.execute_reply": "2021-08-16T21:43:46.816920Z",
     "shell.execute_reply.started": "2021-08-16T20:16:06.195623Z"
    },
    "papermill": {
     "duration": 2.288112,
     "end_time": "2021-08-16T21:43:46.817200",
     "exception": false,
     "start_time": "2021-08-16T21:43:44.529088",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type='text/css'>\n",
       ".datatable table.frame { margin-bottom: 0; }\n",
       ".datatable table.frame thead { border-bottom: none; }\n",
       ".datatable table.frame tr.coltypes td {  color: #FFFFFF;  line-height: 6px;  padding: 0 0.5em;}\n",
       ".datatable .bool    { background: #DDDD99; }\n",
       ".datatable .object  { background: #565656; }\n",
       ".datatable .int     { background: #5D9E5D; }\n",
       ".datatable .float   { background: #4040CC; }\n",
       ".datatable .str     { background: #CC4040; }\n",
       ".datatable .time    { background: #40CC40; }\n",
       ".datatable .row_index {  background: var(--jp-border-color3);  border-right: 1px solid var(--jp-border-color0);  color: var(--jp-ui-font-color3);  font-size: 9px;}\n",
       ".datatable .frame tbody td { text-align: left; }\n",
       ".datatable .frame tr.coltypes .row_index {  background: var(--jp-border-color0);}\n",
       ".datatable th:nth-child(2) { padding-left: 12px; }\n",
       ".datatable .hellipsis {  color: var(--jp-cell-editor-border-color);}\n",
       ".datatable .vellipsis {  background: var(--jp-layout-color0);  color: var(--jp-cell-editor-border-color);}\n",
       ".datatable .na {  color: var(--jp-cell-editor-border-color);  font-size: 80%;}\n",
       ".datatable .sp {  opacity: 0.25;}\n",
       ".datatable .footer { font-size: 9px; }\n",
       ".datatable .frame_dimensions {  background: var(--jp-border-color3);  border-top: 1px solid var(--jp-border-color0);  color: var(--jp-ui-font-color3);  display: inline-block;  opacity: 0.6;  padding: 1px 10px 1px 5px;}\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Importing core libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from time import time\n",
    "import pprint\n",
    "import joblib\n",
    "from functools import partial\n",
    "\n",
    "# Suppressing warnings because of skopt verbosity\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# Classifiers\n",
    "import lightgbm as lgb\n",
    "\n",
    "# Model selection\n",
    "from sklearn.model_selection import KFold\n",
    "\n",
    "# Metrics\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import make_scorer\n",
    "\n",
    "# Skopt functions\n",
    "from skopt import BayesSearchCV\n",
    "from skopt.callbacks import DeadlineStopper, DeltaYStopper\n",
    "from skopt.space import Real, Categorical, Integer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5cc6710e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-16T21:43:46.872385Z",
     "iopub.status.busy": "2021-08-16T21:43:46.871807Z",
     "iopub.status.idle": "2021-08-16T21:43:50.130792Z",
     "shell.execute_reply": "2021-08-16T21:43:50.130210Z",
     "shell.execute_reply.started": "2021-08-16T20:05:46.557705Z"
    },
    "papermill": {
     "duration": 3.286514,
     "end_time": "2021-08-16T21:43:50.130930",
     "exception": false,
     "start_time": "2021-08-16T21:43:46.844416",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Loading data \n",
    "X = pd.read_csv(\"../input/30-days-of-ml/train.csv\")\n",
    "X_test = pd.read_csv(\"../input/30-days-of-ml/test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "74940691",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-16T21:43:50.184626Z",
     "iopub.status.busy": "2021-08-16T21:43:50.184071Z",
     "iopub.status.idle": "2021-08-16T21:43:50.331440Z",
     "shell.execute_reply": "2021-08-16T21:43:50.330750Z"
    },
    "papermill": {
     "duration": 0.175201,
     "end_time": "2021-08-16T21:43:50.331599",
     "exception": false,
     "start_time": "2021-08-16T21:43:50.156398",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Preparing data as a tabular matrix\n",
    "y = X.target\n",
    "X = X.set_index('id').drop('target', axis='columns')\n",
    "X_test = X_test.set_index('id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c6e7723d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-16T21:43:50.417109Z",
     "iopub.status.busy": "2021-08-16T21:43:50.416075Z",
     "iopub.status.idle": "2021-08-16T21:44:04.864778Z",
     "shell.execute_reply": "2021-08-16T21:44:04.864028Z",
     "shell.execute_reply.started": "2021-08-16T20:10:15.868476Z"
    },
    "papermill": {
     "duration": 14.506362,
     "end_time": "2021-08-16T21:44:04.864920",
     "exception": false,
     "start_time": "2021-08-16T21:43:50.358558",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Dealing with categorical data\n",
    "categoricals = [item for item in X.columns if 'cat' in item]\n",
    "cat_values = np.unique(X[categoricals].values)\n",
    "cat_dict = dict(zip(cat_values, range(len(cat_values))))\n",
    "\n",
    "X[categoricals] = X[categoricals].replace(cat_dict).astype('category')\n",
    "X_test[categoricals] = X_test[categoricals].replace(cat_dict).astype('category')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b4441ec",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-16T21:38:41.420722Z",
     "iopub.status.busy": "2021-08-16T21:38:41.420190Z",
     "iopub.status.idle": "2021-08-16T21:38:41.424831Z",
     "shell.execute_reply": "2021-08-16T21:38:41.423574Z",
     "shell.execute_reply.started": "2021-08-16T21:38:41.420637Z"
    },
    "papermill": {
     "duration": 0.027293,
     "end_time": "2021-08-16T21:44:04.918826",
     "exception": false,
     "start_time": "2021-08-16T21:44:04.891533",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Setting up optimization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "888166cf",
   "metadata": {
    "papermill": {
     "duration": 0.026543,
     "end_time": "2021-08-16T21:44:04.972274",
     "exception": false,
     "start_time": "2021-08-16T21:44:04.945731",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "First, we create a wrapper function to deal with running the optimizer and reporting back its best results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6bbb2f57",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-16T21:44:05.034762Z",
     "iopub.status.busy": "2021-08-16T21:44:05.034058Z",
     "iopub.status.idle": "2021-08-16T21:44:05.037113Z",
     "shell.execute_reply": "2021-08-16T21:44:05.036600Z",
     "shell.execute_reply.started": "2021-08-16T20:18:00.467517Z"
    },
    "papermill": {
     "duration": 0.03799,
     "end_time": "2021-08-16T21:44:05.037245",
     "exception": false,
     "start_time": "2021-08-16T21:44:04.999255",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Reporting util for different optimizers\n",
    "def report_perf(optimizer, X, y, title=\"model\", callbacks=None):\n",
    "    \"\"\"\n",
    "    A wrapper for measuring time and performance of optmizers\n",
    "    \n",
    "    optimizer = a sklearn or a skopt optimizer\n",
    "    X = the training set \n",
    "    y = our target\n",
    "    title = a string label for the experiment\n",
    "    \"\"\"\n",
    "    start = time()\n",
    "    \n",
    "    if callbacks is not None:\n",
    "        optimizer.fit(X, y, callback=callbacks)\n",
    "    else:\n",
    "        optimizer.fit(X, y)\n",
    "        \n",
    "    d=pd.DataFrame(optimizer.cv_results_)\n",
    "    best_score = optimizer.best_score_\n",
    "    best_score_std = d.iloc[optimizer.best_index_].std_test_score\n",
    "    best_params = optimizer.best_params_\n",
    "    \n",
    "    print((title + \" took %.2f seconds,  candidates checked: %d, best CV score: %.3f \"\n",
    "           + u\"\\u00B1\"+\" %.3f\") % (time() - start, \n",
    "                                   len(optimizer.cv_results_['params']),\n",
    "                                   best_score,\n",
    "                                   best_score_std))    \n",
    "    print('Best parameters:')\n",
    "    pprint.pprint(best_params)\n",
    "    print()\n",
    "    return best_params"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc15791e",
   "metadata": {
    "papermill": {
     "duration": 0.026374,
     "end_time": "2021-08-16T21:44:05.090383",
     "exception": false,
     "start_time": "2021-08-16T21:44:05.064009",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We then define the evaluation metric, using the Scikit-learn function make_scorer allows us to convert the optimization into a minimization problem, as required by Scikit-optimize. We set squared=False by means of a partial function to obtain the root mean squared error (RMSE) as evaluation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2429ab87",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-16T21:44:05.149451Z",
     "iopub.status.busy": "2021-08-16T21:44:05.148805Z",
     "iopub.status.idle": "2021-08-16T21:44:05.151537Z",
     "shell.execute_reply": "2021-08-16T21:44:05.151048Z",
     "shell.execute_reply.started": "2021-08-16T20:15:26.345461Z"
    },
    "papermill": {
     "duration": 0.033729,
     "end_time": "2021-08-16T21:44:05.151672",
     "exception": false,
     "start_time": "2021-08-16T21:44:05.117943",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Setting the scoring function\n",
    "scoring = make_scorer(partial(mean_squared_error, squared=False), \n",
    "                      greater_is_better=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29615d10",
   "metadata": {
    "papermill": {
     "duration": 0.026116,
     "end_time": "2021-08-16T21:44:05.204708",
     "exception": false,
     "start_time": "2021-08-16T21:44:05.178592",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We set up a  5-fold cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a273b703",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-16T21:44:05.262517Z",
     "iopub.status.busy": "2021-08-16T21:44:05.261886Z",
     "iopub.status.idle": "2021-08-16T21:44:05.263619Z",
     "shell.execute_reply": "2021-08-16T21:44:05.264166Z",
     "shell.execute_reply.started": "2021-08-16T20:16:09.267743Z"
    },
    "papermill": {
     "duration": 0.032949,
     "end_time": "2021-08-16T21:44:05.264314",
     "exception": false,
     "start_time": "2021-08-16T21:44:05.231365",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Setting the validation strategy\n",
    "kf = KFold(n_splits=5, shuffle=True, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "542a794f",
   "metadata": {
    "papermill": {
     "duration": 0.026099,
     "end_time": "2021-08-16T21:44:05.316927",
     "exception": false,
     "start_time": "2021-08-16T21:44:05.290828",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We set up a generic LightGBM regressor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dda83cbc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-16T21:44:05.373262Z",
     "iopub.status.busy": "2021-08-16T21:44:05.372685Z",
     "iopub.status.idle": "2021-08-16T21:44:05.376875Z",
     "shell.execute_reply": "2021-08-16T21:44:05.377433Z",
     "shell.execute_reply.started": "2021-08-16T20:16:43.455445Z"
    },
    "papermill": {
     "duration": 0.033683,
     "end_time": "2021-08-16T21:44:05.377607",
     "exception": false,
     "start_time": "2021-08-16T21:44:05.343924",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Setting the basic regressor\n",
    "reg = lgb.LGBMRegressor(boosting_type='gbdt',\n",
    "                        metric='rmse',\n",
    "                        objective='regression',\n",
    "                        n_jobs=1, \n",
    "                        verbose=-1,\n",
    "                        random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "395d929c",
   "metadata": {
    "papermill": {
     "duration": 0.026215,
     "end_time": "2021-08-16T21:44:05.430171",
     "exception": false,
     "start_time": "2021-08-16T21:44:05.403956",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We define a search space, expliciting the key hyper-parameters to optimize and the range where to look for the best values.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fda60f42",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-16T21:44:05.486543Z",
     "iopub.status.busy": "2021-08-16T21:44:05.485891Z",
     "iopub.status.idle": "2021-08-16T21:44:05.502831Z",
     "shell.execute_reply": "2021-08-16T21:44:05.503353Z",
     "shell.execute_reply.started": "2021-08-16T20:17:11.688734Z"
    },
    "papermill": {
     "duration": 0.046542,
     "end_time": "2021-08-16T21:44:05.503539",
     "exception": false,
     "start_time": "2021-08-16T21:44:05.456997",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Setting the search space\n",
    "search_spaces = {\n",
    "    'learning_rate': Real(0.01, 1.0, 'log-uniform'),     # Boosting learning rate\n",
    "    'n_estimators': Integer(30, 5000),                   # Number of boosted trees to fit\n",
    "    'num_leaves': Integer(2, 512),                       # Maximum tree leaves for base learners\n",
    "    'max_depth': Integer(-1, 256),                       # Maximum tree depth for base learners, <=0 means no limit\n",
    "    'min_child_samples': Integer(1, 256),                # Minimal number of data in one leaf\n",
    "    'max_bin': Integer(100, 1000),                       # Max number of bins that feature values will be bucketed\n",
    "    'subsample': Real(0.01, 1.0, 'uniform'),             # Subsample ratio of the training instance\n",
    "    'subsample_freq': Integer(0, 10),                    # Frequency of subsample, <=0 means no enable\n",
    "    'colsample_bytree': Real(0.01, 1.0, 'uniform'),      # Subsample ratio of columns when constructing each tree\n",
    "    'min_child_weight': Real(0.01, 10.0, 'uniform'),     # Minimum sum of instance weight (hessian) needed in a child (leaf)\n",
    "    'reg_lambda': Real(1e-9, 100.0, 'log-uniform'),      # L2 regularization\n",
    "    'reg_alpha': Real(1e-9, 100.0, 'log-uniform'),       # L1 regularization\n",
    "   }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "738f15b1",
   "metadata": {
    "papermill": {
     "duration": 0.026225,
     "end_time": "2021-08-16T21:44:05.556628",
     "exception": false,
     "start_time": "2021-08-16T21:44:05.530403",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We then define the Bayesian optimization engine, providing to it our LightGBM, the search spaces, the evaluation metric, the cross-validation. We set a large number of possible experiments and some parallelism in the search operations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "66cb854f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-16T21:44:05.612727Z",
     "iopub.status.busy": "2021-08-16T21:44:05.612055Z",
     "iopub.status.idle": "2021-08-16T21:44:05.617142Z",
     "shell.execute_reply": "2021-08-16T21:44:05.617621Z",
     "shell.execute_reply.started": "2021-08-16T20:17:34.864656Z"
    },
    "papermill": {
     "duration": 0.034387,
     "end_time": "2021-08-16T21:44:05.617770",
     "exception": false,
     "start_time": "2021-08-16T21:44:05.583383",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Wrapping everything up into the Bayesian optimizer\n",
    "opt = BayesSearchCV(estimator=reg,                                    \n",
    "                    search_spaces=search_spaces,                      \n",
    "                    scoring=scoring,                                  \n",
    "                    cv=kf,                                           \n",
    "                    n_iter=60,                                        # max number of trials\n",
    "                    n_points=3,                                       # number of hyperparameter sets evaluated at the same time\n",
    "                    n_jobs=-1,                                        # number of jobs\n",
    "                    iid=False,                                        # if not iid it optimizes on the cv score\n",
    "                    return_train_score=False,                         \n",
    "                    refit=False,                                      \n",
    "                    optimizer_kwargs={'base_estimator': 'GP'},        # optmizer parameters: we use Gaussian Process (GP)\n",
    "                    random_state=0)                                   # random state for replicability"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31217f03",
   "metadata": {
    "papermill": {
     "duration": 0.026284,
     "end_time": "2021-08-16T21:44:05.670703",
     "exception": false,
     "start_time": "2021-08-16T21:44:05.644419",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Finally we runt the optimizer and wait for the results. We have set some limits to its operations: we required it to stop if it cannot get consistent improvements from the search (DeltaYStopper) and time dealine set in seconds (we decided for 6 hours)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8df19cf1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-16T21:44:05.726573Z",
     "iopub.status.busy": "2021-08-16T21:44:05.725972Z",
     "iopub.status.idle": "2021-08-17T03:30:32.980029Z",
     "shell.execute_reply": "2021-08-17T03:30:32.980640Z",
     "shell.execute_reply.started": "2021-08-16T20:18:04.081194Z"
    },
    "papermill": {
     "duration": 20787.283763,
     "end_time": "2021-08-17T03:30:32.980917",
     "exception": false,
     "start_time": "2021-08-16T21:44:05.697154",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LightGBM_regression took 20787.21 seconds,  candidates checked: 42, best CV score: -0.721 ± 0.002\n",
      "Best parameters:\n",
      "OrderedDict([('colsample_bytree', 0.01),\n",
      "             ('learning_rate', 1.0),\n",
      "             ('max_bin', 156),\n",
      "             ('max_depth', -1),\n",
      "             ('min_child_samples', 241),\n",
      "             ('min_child_weight', 9.715188106585181),\n",
      "             ('n_estimators', 5000),\n",
      "             ('num_leaves', 192),\n",
      "             ('reg_alpha', 1e-09),\n",
      "             ('reg_lambda', 1e-09),\n",
      "             ('subsample', 1.0),\n",
      "             ('subsample_freq', 1)])\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Running the optimizer\n",
    "overdone_control = DeltaYStopper(delta=0.0001)               # We stop if the gain of the optimization becomes too small\n",
    "time_limit_control = DeadlineStopper(total_time=60 * 60 * 6) # We impose a time limit (6 hours)\n",
    "\n",
    "best_params = report_perf(opt, X, y,'LightGBM_regression', \n",
    "                          callbacks=[overdone_control, time_limit_control])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13214429",
   "metadata": {
    "papermill": {
     "duration": 0.028022,
     "end_time": "2021-08-17T03:30:33.037907",
     "exception": false,
     "start_time": "2021-08-17T03:30:33.009885",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Prediction on test data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a22c018",
   "metadata": {
    "papermill": {
     "duration": 0.026938,
     "end_time": "2021-08-17T03:30:33.093399",
     "exception": false,
     "start_time": "2021-08-17T03:30:33.066461",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Having got the best hyperparameters for the data at hand, we instantiate a lightGBM using such values and train our model on all the available examples.\n",
    "\n",
    "After having trained the model, we predict on the test set and we save the results on a csv file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "286c35be",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-17T03:30:33.154589Z",
     "iopub.status.busy": "2021-08-17T03:30:33.153917Z",
     "iopub.status.idle": "2021-08-17T03:30:33.157133Z",
     "shell.execute_reply": "2021-08-17T03:30:33.156650Z",
     "shell.execute_reply.started": "2021-08-16T20:43:50.113733Z"
    },
    "papermill": {
     "duration": 0.036532,
     "end_time": "2021-08-17T03:30:33.157267",
     "exception": false,
     "start_time": "2021-08-17T03:30:33.120735",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Transferring the best parameters to our basic regressor\n",
    "reg = lgb.LGBMRegressor(boosting_type='gbdt',\n",
    "                        metric='rmse',\n",
    "                        objective='regression',\n",
    "                        n_jobs=1, \n",
    "                        verbose=-1,\n",
    "                        random_state=0,\n",
    "                         **best_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "27c22939",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-17T03:30:33.218900Z",
     "iopub.status.busy": "2021-08-17T03:30:33.218239Z",
     "iopub.status.idle": "2021-08-17T03:32:18.277019Z",
     "shell.execute_reply": "2021-08-17T03:32:18.277547Z",
     "shell.execute_reply.started": "2021-08-16T20:47:04.261195Z"
    },
    "papermill": {
     "duration": 105.093262,
     "end_time": "2021-08-17T03:32:18.277746",
     "exception": false,
     "start_time": "2021-08-17T03:30:33.184484",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMRegressor(colsample_bytree=0.01, learning_rate=1.0, max_bin=156,\n",
       "              metric='rmse', min_child_samples=241,\n",
       "              min_child_weight=9.715188106585181, n_estimators=5000, n_jobs=1,\n",
       "              num_leaves=192, objective='regression', random_state=0,\n",
       "              reg_alpha=1e-09, reg_lambda=1e-09, subsample_freq=1, verbose=-1)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting the regressor on all the data\n",
    "reg.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "257a1dad",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-17T03:32:18.340375Z",
     "iopub.status.busy": "2021-08-17T03:32:18.339691Z",
     "iopub.status.idle": "2021-08-17T03:37:09.143483Z",
     "shell.execute_reply": "2021-08-17T03:37:09.142719Z",
     "shell.execute_reply.started": "2021-08-16T20:47:12.733882Z"
    },
    "papermill": {
     "duration": 290.837305,
     "end_time": "2021-08-17T03:37:09.143692",
     "exception": false,
     "start_time": "2021-08-17T03:32:18.306387",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Preparing the submission\n",
    "submission = pd.DataFrame({'id':X_test.index, \n",
    "                           'target': reg.predict(X_test).ravel()})\n",
    "\n",
    "submission.to_csv(\"submission.csv\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "31e2c64b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-17T03:37:09.209958Z",
     "iopub.status.busy": "2021-08-17T03:37:09.209278Z",
     "iopub.status.idle": "2021-08-17T03:37:09.230436Z",
     "shell.execute_reply": "2021-08-17T03:37:09.229783Z",
     "shell.execute_reply.started": "2021-08-16T21:42:33.459014Z"
    },
    "papermill": {
     "duration": 0.057576,
     "end_time": "2021-08-17T03:37:09.230592",
     "exception": false,
     "start_time": "2021-08-17T03:37:09.173016",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>8.164113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>8.461944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>8.413098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16</td>\n",
       "      <td>8.431498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>8.260932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199995</th>\n",
       "      <td>499987</td>\n",
       "      <td>8.139140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199996</th>\n",
       "      <td>499990</td>\n",
       "      <td>8.451309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199997</th>\n",
       "      <td>499991</td>\n",
       "      <td>8.506756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199998</th>\n",
       "      <td>499994</td>\n",
       "      <td>8.197716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199999</th>\n",
       "      <td>499995</td>\n",
       "      <td>8.045889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id    target\n",
       "0            0  8.164113\n",
       "1            5  8.461944\n",
       "2           15  8.413098\n",
       "3           16  8.431498\n",
       "4           17  8.260932\n",
       "...        ...       ...\n",
       "199995  499987  8.139140\n",
       "199996  499990  8.451309\n",
       "199997  499991  8.506756\n",
       "199998  499994  8.197716\n",
       "199999  499995  8.045889\n",
       "\n",
       "[200000 rows x 2 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 21213.764184,
   "end_time": "2021-08-17T03:37:10.371215",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2021-08-16T21:43:36.607031",
   "version": "2.3.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
